LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866911442512379904 |
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| author | Lin, Tianhe Xiong, Ziwei Ou, Baoyuan Qin, Yingjie Xu, Lai Zhong, Xiaocheng Hu, Yao Wang, Zhiyong Zhou, Tao Xu, Yubin Wu, Di |
| author_facet | Lin, Tianhe Xiong, Ziwei Ou, Baoyuan Qin, Yingjie Xu, Lai Zhong, Xiaocheng Hu, Yao Wang, Zhiyong Zhou, Tao Xu, Yubin Wu, Di |
| contents | Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time industrial environments faces a strict "Latency Wall", constrained by two distinct bottlenecks: the high I/O latency of retrieving massive user histories and the quadratic computational complexity of standard attention mechanisms. To break these bottlenecks, we present LASER, a full-stack optimization framework developed and deployed at Xiaohongshu (RedNote). Our approach tackles the challenges through two complementary innovations: (1) System efficiency: We introduce SeqVault, a unified schema-aware serving infrastructure for long user histories. By implementing a hybrid DRAM-SSD indexing strategy, SeqVault reduces retrieval latency by 50% and CPU usage by 75%, ensuring millisecond-level access to full real-time and life-cycle user histories. (2) Algorithmic efficiency: We propose a Segmented Target Attention (STA) mechanism to address the computational overhead. Motivated by the inherent sparsity of user interests, STA employs a sigmoid-based gating strategy that acts as a silence mechanism to filter out noisy items. Subsequently, a lightweight Global Stacked Target Attention (GSTA) module refines these compressed segments to capture cross-segment dependencies without incurring high computational costs. This design performs effective sequence compression, reducing the complexity of long-sequence modeling while preserving critical signals. Extensive offline evaluations demonstrate that LASER consistently outperforms state-of-the-art baselines. In large-scale online A/B testing serving over 100 million daily active users, LASER achieved a 2.36% lift in ADVV and a 2.08% lift in revenue, demonstrating its scalability and significant commercial impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11562 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling Lin, Tianhe Xiong, Ziwei Ou, Baoyuan Qin, Yingjie Xu, Lai Zhong, Xiaocheng Hu, Yao Wang, Zhiyong Zhou, Tao Xu, Yubin Wu, Di Information Retrieval Modeling ultra-long user behavior sequences is pivotal for capturing evolving and lifelong interests in modern recommendation systems. However, deploying such models in real-time industrial environments faces a strict "Latency Wall", constrained by two distinct bottlenecks: the high I/O latency of retrieving massive user histories and the quadratic computational complexity of standard attention mechanisms. To break these bottlenecks, we present LASER, a full-stack optimization framework developed and deployed at Xiaohongshu (RedNote). Our approach tackles the challenges through two complementary innovations: (1) System efficiency: We introduce SeqVault, a unified schema-aware serving infrastructure for long user histories. By implementing a hybrid DRAM-SSD indexing strategy, SeqVault reduces retrieval latency by 50% and CPU usage by 75%, ensuring millisecond-level access to full real-time and life-cycle user histories. (2) Algorithmic efficiency: We propose a Segmented Target Attention (STA) mechanism to address the computational overhead. Motivated by the inherent sparsity of user interests, STA employs a sigmoid-based gating strategy that acts as a silence mechanism to filter out noisy items. Subsequently, a lightweight Global Stacked Target Attention (GSTA) module refines these compressed segments to capture cross-segment dependencies without incurring high computational costs. This design performs effective sequence compression, reducing the complexity of long-sequence modeling while preserving critical signals. Extensive offline evaluations demonstrate that LASER consistently outperforms state-of-the-art baselines. In large-scale online A/B testing serving over 100 million daily active users, LASER achieved a 2.36% lift in ADVV and a 2.08% lift in revenue, demonstrating its scalability and significant commercial impact. |
| title | LASER: An Efficient Target-Aware Segmented Attention Framework for End-to-End Long Sequence Modeling |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.11562 |